The CUR decomposition method aims to approximate a matrix with low error using only a few of its columns. It is similar to sparse PCA methods but uses a randomized approach instead of optimization. The method is actually optimizing a sparse regression objective and has a unique sparsity structure. This study shows that CUR cannot be directly used as a sparse PCA method but can inspire new methods with similar sparsity properties.